Transfer your Font Style Using Multi-Content GAN

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

10 years of news and resources for members of the IEEE Signal Processing Society

Transfer your Font Style Using Multi-Content GAN

Researchers from UC Berkeley have developed a deep learning model that can automatically transfer your font style. The model is named multi-content generative adversarial network (MC-GAN). It consists of a stacked conditional generative adversarial network (cGAN) architecture to predict the coarse glyph shapes and an ornamentation network to predict color and texture of the final glyphs. As an example, it can generate a new title with the same style from a movie poster.

Courtesy of the researchers

This is not the first time deep learning has been used for style transfer. For example, convolutional neural networks (CNN) can be used for image style transfer (see

For more details about MC-GAN, please visit their blog at

SPS on Twitter

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar